where \(w_c\)  s are the weights of the classification module, \(C\)  is the number of action classes in the entire dataset, and \(y\)  is the label of action that happens in the video. Also, label 0 represents the background class. Similarly, \(\mathcal {L}_{bg}\)  is defined for \(p_{bg}\)  which is a softmax applied over multiplication of background feature \(b\)  and the classification module. This loss trains attention vector \(\lambda \)  through class-agnostic features \(f\)  and \(b\)  (def REF ), and is used in STPN [1]}.
